Monitoring business revenues and managing change in revenue with Elastic Machine Learning anomaly detection.

The new Elasticsearch Query Language is a flexible, powerful, and robust query expression language to interrogate data. In this session learn how ESQL provides a superior query UX, a piped query language with join capabilities that fundamentally transforms and expands the analytics and data processing of Elasticsearch.

Do you understand the differences between Elasticsearch and OpenSearch? We’ll lay them out for you. You’ll find that our take on emerging technologies is fundamentally transforming the opportunity to solve problems through search. Learn about innovation in areas like vector search and hybrid scoring or support for third-party natural language processing that help you unlock possibilities for new classes of searches through the application of machine learning. The result? Increased relevance with less burden on the developer and administrator. In this session, you'll learn all about these innovations, and how you can take advantage of them to drive success.

Finding matches when using a taxonomy is a common problem. A notable challenge is mapping a user’s query to the entity (or results) expected when searching for an entity inside a catalog mapping. Functional textual search models tend to rely on exact match or partial match, but both can lead to a frustrating experience when users aren’t familiar with the domain. Basic models often fail to support user typos, synonyms, acronyms, and/or hyponyms/hypernyms. Learn how to tackle these challenges and make search more intuitive when using a taxonomy.

Elasticsearch supports both rectangular and hexagonal grids when aggregating spatial data geographically. In this video, we compare and contrast these two approaches in Kibana using both geo_point and geo_shape data sources. To highlight this, we also make use of Kibana’s new map synchronization feature.

Geographic grid systems allow zooming into maps at progressively higher resolutions and finer grids. For rectangular grids, this is very simple, but for hexagonal grids, the situation is much more complex, since child hexagons are not fully contained within parent hexagons. This video demonstrates how we can still achieve efficient parent-child search in Elasticsearch using the H3 hexagonal grid.

Elasticsearch supports both rectangular and hexagonal grids when aggregating spatial data geographically. This video shows how to set up a Kibana dashboard with four synchronized maps with appropriate layer configurations for comparing and contrasting these two approaches using both geo_point and geo_shape data sources.

Learn how to ingest Nginx logs using Elastic agent and nginx integration and view metrics in OOTB dashboards.

Learn how Elastic observability can help you understand the user's journey and experiences on Elastic Observability's Synthetics Monitoring.

Ken Exner, Chief Product Officer at Elastic shares where we've been and where we’re heading as a company during 2023 ElasticON Global. In this opening keynote, Ken highlights key innovation areas in our observability and security solutions, with a demo of ESQL, and closes off by sharing our current journey of building out a serverless offering.